CN111873993A - Vehicle driving risk early warning method based on big data - Google Patents

Vehicle driving risk early warning method based on big data Download PDF

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Publication number
CN111873993A
CN111873993A CN202010645186.1A CN202010645186A CN111873993A CN 111873993 A CN111873993 A CN 111873993A CN 202010645186 A CN202010645186 A CN 202010645186A CN 111873993 A CN111873993 A CN 111873993A
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China
Prior art keywords
vehicle
early warning
data
vehicles
danger
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Withdrawn
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CN202010645186.1A
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Chinese (zh)
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毕远强
王长军
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Shanghai Wanwei Technology Co ltd
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Shanghai Wanwei Technology Co ltd
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Priority to CN202010645186.1A priority Critical patent/CN111873993A/en
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Withdrawn legal-status Critical Current

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/095—Predicting travel path or likelihood of collision
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/095—Predicting travel path or likelihood of collision
    • B60W30/0953—Predicting travel path or likelihood of collision the prediction being responsive to vehicle dynamic parameters
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—Speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
    • B60W40/13—Load or weight
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/0097—Predicting future conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/0098—Details of control systems ensuring comfort, safety or stability not otherwise provided for
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0062—Adapting control system settings
    • B60W2050/0075—Automatic parameter input, automatic initialising or calibrating means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/06—Combustion engines, Gas turbines
    • B60W2510/0638—Engine speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2530/00—Input parameters relating to vehicle conditions or values, not covered by groups B60W2510/00 or B60W2520/00
    • B60W2530/10—Weight
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2530/00—Input parameters relating to vehicle conditions or values, not covered by groups B60W2510/00 or B60W2520/00
    • B60W2530/20—Tyre data
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B60W2554/404—Characteristics
    • B60W2554/4041—Position
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B60W2554/404—Characteristics
    • B60W2554/4042—Longitudinal speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/80—Spatial relation or speed relative to objects
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2556/00—Input parameters relating to data
    • B60W2556/45—External transmission of data to or from the vehicle
    • B60W2556/65—Data transmitted between vehicles

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Human Computer Interaction (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention discloses a big data-based vehicle driving risk early warning method, which comprises the following steps: acquiring running data of a vehicle, wherein the running data comprises overweight data, overspeed data, engine rotating speed data, water temperature data and tire pressure data; comparing each item of operation data of the vehicle with a danger threshold corresponding to the item of operation data, and identifying the vehicle in a dangerous driving state; and sending danger early warning to other vehicles. And further, a danger early warning object can be accurately determined according to the alarm distance or the Bluetooth connection record. The driving risk of the vehicle is identified through data analysis, the vehicles which are close to the vehicle and in a dangerous driving state are found out, air risk early warning is sent, and targeted driving risk early warning is achieved. Other vehicles near the vehicle in the dangerous driving state can take measures in time according to risk early warning, so that accidents are avoided.

Description

Vehicle driving risk early warning method based on big data
Technical Field
The invention belongs to the field of vehicle driving risk early warning, and particularly relates to a vehicle driving risk early warning method based on big data.
Background
At present, technologies such as voice navigation, Bluetooth communication, vehicle risk control through a GPS terminal device and the like are relatively mature. However, the existing technical solutions of software and hardware for serving vehicles can only monitor dangerous behaviors of vehicles by regularly or irregularly acquiring road conditions, cannot assist vehicles in real time to avoid risks caused by surrounding environments, and cannot early warn dangerous vehicles around the vehicles in advance.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a vehicle driving risk early warning method based on big data.
The technical scheme adopted by the invention for solving the technical problems is as follows: a big data-based vehicle driving risk early warning method comprises the following steps:
acquiring running data of a vehicle, wherein the running data comprises overweight data, overspeed data, engine rotating speed data, water temperature data and tire pressure data;
comparing each item of operation data of the vehicle with a danger threshold corresponding to the item of operation data, and identifying the vehicle in a dangerous driving state;
and sending danger early warning to other vehicles.
Further, position data of the vehicle is acquired, an alarm distance is set, the distance between the other vehicle and the vehicle in the dangerous driving state is compared with the alarm distance, and a dangerous early warning is sent to the other vehicle within the alarm distance.
Furthermore, in the driving process, communication connection can be established between adjacent vehicles through short-distance transmission protocols such as Bluetooth and the like, and the Bluetooth equipment address of each vehicle is the unique identification of the vehicle;
and acquiring the Bluetooth communication record of the vehicle in the dangerous driving state, and sending a dangerous early warning to other vehicles which establish Bluetooth connection with the vehicle.
Further, for each item of operational data, the hazard threshold comprises: a general hazard threshold and an emergency hazard threshold;
and triggering common danger early warning or emergency danger early warning of the corresponding grade according to the grade of the danger threshold value reached by the running data of the vehicle.
Further, when the dangerous driving state of vehicle is discerned, still including the brake detection information who obtains this vehicle, including the water content of brake block wearing and tearing degree and brake fluid to judge whether braking system is out of order, trigger dangerous early warning when having the trouble.
Further, when the dangerous driving state of the vehicle is identified, the collision state of the vehicle is acquired, and when the state information that the vehicle collides is acquired, the emergency danger early warning is immediately sent to other vehicles.
Further, the distance between adjacent vehicles and the speed of the vehicles are obtained, and the collision time T is calculated1Or inter-vehicle distance time T2And comparing the collision time or the inter-vehicle distance time with a corresponding danger threshold value, identifying collision danger, and triggering danger early warning when the collision danger exists.
The invention also provides computer equipment, which comprises an input and output unit, a memory and a processor, wherein computer readable instructions are stored in the memory, and when the computer readable instructions are executed by the processor, the processor executes the steps in the big data-based vehicle driving risk early warning method.
The invention also provides a storage medium storing computer readable instructions, which when executed by one or more processors, cause the one or more processors to execute the steps of the big data-based vehicle driving risk early warning method according to the above technical solution.
Compared with the prior art, the invention has the beneficial effects that: and identifying the driving risk of the vehicle through data analysis, finding out the vehicle which is in a dangerous driving state and is close to the vehicle, and sending risk early warning. According to the alarm distance or the Bluetooth connection record, dangerous early warning objects can be accurately determined, and accordingly targeted driving risk early warning is achieved. And, in addition to alerting for real-time collision hazards, potential hazards are identified through data analysis. Other vehicles near the vehicle in the dangerous driving state can take measures in time according to risk early warning, so that accidents are avoided.
Drawings
Fig. 1 is a schematic flow chart of a big data-based vehicle driving risk early warning method according to an embodiment.
Detailed Description
The invention is further described below with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, the big data-based vehicle driving risk early warning method of the embodiment includes:
the method comprises the steps of obtaining running data of the vehicle, wherein the running data comprises overweight data, overspeed data, engine rotating speed data, water temperature data, tire pressure data and the like.
And setting a danger threshold value for each item of operation data, comparing the operation data of the vehicle with the corresponding danger threshold value, judging the operation condition of the vehicle, and identifying the vehicle in a dangerous driving state.
When vehicles in dangerous driving states exist, a dangerous area is arranged in a certain range nearby the vehicles, traffic accidents are prone to happening, dangerous early warning can be sent to other vehicles, and the dangerous area is avoided in time. The obtaining of various data of the vehicle or the sending of the danger warning to the vehicle all means establishing a communication connection with a network device configured on the vehicle to implement the sending or receiving of the data, which is a conventional technical means in the field and is not described herein again.
Further, position data of the vehicle is acquired, an alarm distance is set, the distance between the other vehicle and the vehicle in the dangerous driving state is compared with the alarm distance, and a dangerous early warning is sent to the other vehicle within the alarm distance.
Or, further, during the driving process, communication connection can be established between adjacent vehicles through a short-distance transmission protocol such as bluetooth. The furthest distance to transmit bluetooth may be taken as the boundary of the hazardous area. And each vehicle has a unique bluetooth device address as a unique identification of the vehicle.
And acquiring the Bluetooth communication record of the vehicle in the dangerous driving state, and sending a dangerous early warning to other vehicles which establish Bluetooth connection with the vehicle.
According to the alarm distance or the Bluetooth connection record, the danger early warning object can be accurately determined. Or, the two ways of determining the early warning object can be combined, so that the dangerous early warning object can be determined more accurately.
Further, the hazard threshold includes two levels for each item of operational data: normal and emergency. The normal danger threshold and the emergency danger threshold reflect the abnormal severity of one piece of operation data, and the higher the abnormal severity, the higher the risk that the vehicle breaks down or sends an accident. Taking the load data as an example, if the upper limit of the load of a vehicle is 2 tons, and the actual load of the vehicle is 2.05 tons, the overweight has a dangerous risk, but the overweight is less, and 2 tons can be used as a common dangerous threshold. An upper load limit of 110% of the vehicle may be used as the emergency hazard threshold. If the actual load of the vehicle is 2.2 tons, the overweight proportion is higher, and the probability of accidents is greatly increased.
And triggering danger early warning of corresponding grade, namely common danger early warning or emergency danger early warning, according to the grade of the danger threshold value reached by the running data of the vehicle. Therefore, when other vehicles receive danger early warning of different levels, different coping modes can be taken pertinently, for example, when emergency danger early warning is received, owners of other vehicles need to take measures in time so as to avoid the vehicles in dangerous driving states.
Furthermore, when the dangerous driving state of the vehicle is identified, the method also comprises the step of obtaining the brake detection information of the vehicle so as to judge whether the brake system has faults. When the brake oil and the brake pad of the vehicle brake system have faults, great potential safety hazards exist. The fault factor of the brake pad is mainly the degree of wear, can adopt current wear sensor monitoring, the fault factor of brake fluid is mainly the water content increase, can adopt current brake fluid detector monitoring. The brake pad is high in abrasion degree, needs to be replaced in time, or is easy to malfunction; the brake fluid has a high water content, which leads to a reduction in braking performance. And if the wear degree of the brake pad of one vehicle is higher or the water content of the brake oil exceeds a danger threshold value, triggering a danger early warning and sending the danger early warning to other vehicles nearby the vehicle. Therefore, other vehicles can keep a certain distance or enlarge the distance according to the danger early warning so as to reduce the sending probability of accidents.
Further, when the dangerous driving state of the vehicle is identified, the method also comprises the step of acquiring the collision state of the vehicle. The crash state may be acquired by an on-vehicle sensor. For the accidents of collision, the occurrence of the accidents is often instantaneous, and when the state information of collision of one vehicle is acquired, an emergency danger early warning is immediately sent to other vehicles. After other vehicles within a certain range away from the collided vehicle receive the danger early warning, measures can be taken in time to avoid more collision accidents.
Still further, in addition to obtaining the real-time collision status of the vehicle, the collision occurrence probability of the vehicle is also estimated. For a certain vehicle, finding out other vehicles establishing Bluetooth communication records with the vehicle within a period of time according to the Bluetooth communication records of the vehicle, wherein at the moment: 1) calculating the distance between vehicles through the position data of the vehicles, and estimating the probability of collision of the vehicles by combining the overspeed or speed data of the vehicles; or, 2) by the vehicle's own collision warning system. When a vehicle is close to another vehicle in distance and the vehicle speed is abnormal (e.g., overspeed, abnormal acceleration, deceleration, etc.), the probability of a collision is relatively high. In particular, the time of collision T can be determined1Or inter-vehicle distance time T2Reflecting the probability of collision, time of collision T1Can pass through the vehicle distance S of the two vehicles and the relative vehicle speed V of the two vehiclesrCalculating the time T between vehicles2Passing through the distance S between two vehicles and the speed V of the vehicle1And (4) calculating. Wherein the time of collision T1The emergency of the collision danger of the vehicle is mainly reflected, namely the vehicle is bound to collide without taking countermeasures; time between vehicles T2Mainly reflecting the potential for collisions to occur, for example: if the inter-vehicle distance time is short, when one of the two vehicles suddenly accelerates or decelerates, a collision occurs. Similarly, the danger early warning can be carried out by setting the corresponding danger threshold of the collision time or the inter-vehicle distance time.
The computer device of the embodiment comprises an input and output unit, a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, enable the processor to execute the steps in the big data-based vehicle driving risk early warning method.
A storage medium of the present embodiment stores computer readable instructions, which when executed by one or more processors, cause the one or more processors to perform the steps of the big data based vehicle driving risk early warning method.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (9)

1. A vehicle driving risk early warning method based on big data is characterized by comprising the following steps:
acquiring running data of a vehicle, wherein the running data comprises overweight data, overspeed data, engine rotating speed data, water temperature data and tire pressure data;
comparing each item of operation data of the vehicle with a danger threshold corresponding to the item of operation data, and identifying the vehicle in a dangerous driving state;
and sending danger early warning to other vehicles.
2. The vehicle driving risk early warning method according to claim 1, further comprising the steps of: the method comprises the steps of obtaining position data of a vehicle, setting an alarm distance, comparing the distance between other vehicles and the vehicle in a dangerous driving state with the alarm distance, and sending a dangerous early warning to other vehicles within the alarm distance.
3. The vehicle driving risk early warning method according to claim 2, wherein in the driving process, communication connection is established between adjacent vehicles through a Bluetooth transmission protocol, and the Bluetooth device address of each vehicle is the unique identifier of the vehicle;
and acquiring the Bluetooth communication record of the vehicle in the dangerous driving state, and sending a dangerous early warning to other vehicles which establish Bluetooth connection with the vehicle.
4. The vehicle driving risk early warning method according to claim 1, wherein the risk threshold includes, for each piece of the operation data: a general hazard threshold and an emergency hazard threshold;
and triggering common danger early warning or emergency danger early warning of the corresponding grade according to the grade of the danger threshold value reached by the running data of the vehicle.
5. The vehicle driving risk early warning method according to claim 1, wherein when the dangerous driving state of the vehicle is identified, the method further comprises the step of obtaining brake detection information of the vehicle, wherein the brake detection information comprises the wear degree of a brake pad and the water content of brake oil, so as to judge whether a brake system has a fault or not, and trigger dangerous early warning when the fault exists.
6. The vehicle driving risk early warning method according to claim 1, wherein when the dangerous driving state of the vehicle is identified, the method further comprises the step of acquiring the collision state of the vehicle, and when the state information that one vehicle collides is acquired, the method immediately sends an emergency danger early warning to other vehicles.
7. The vehicle driving risk early warning method according to claim 6, wherein the distance between adjacent vehicles and the speed of the vehicles are obtained, and the collision time T is calculated1Or inter-vehicle distance time T2And comparing the collision time or the inter-vehicle distance time with a corresponding danger threshold value, identifying collision danger, and triggering danger early warning when the collision danger exists.
8. A computer device comprising an input/output unit, a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the steps of a big data based vehicle driving risk early warning method according to the above technical solution.
9. A storage medium storing computer readable instructions, wherein the computer readable instructions, when executed by one or more processors, cause the one or more processors to perform the steps of the big data based vehicle driving risk early warning method according to the foregoing technical solution.
CN202010645186.1A 2020-07-07 2020-07-07 Vehicle driving risk early warning method based on big data Withdrawn CN111873993A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113536949A (en) * 2021-06-21 2021-10-22 上汽通用五菱汽车股份有限公司 Accident risk level evaluation method and device and computer readable storage medium

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113536949A (en) * 2021-06-21 2021-10-22 上汽通用五菱汽车股份有限公司 Accident risk level evaluation method and device and computer readable storage medium

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